A novel data-driven deep learning approach for wind turbine power curve modeling
Yun Wang,
Xiaocong Duan,
Runmin Zou,
Fan Zhang,
Yifen Li and
Qinghua Hu
Energy, 2023, vol. 270, issue C
Abstract:
Existing wind turbine power curve (WTPC) models have limited performance in capturing the complex relationship between wind speed and wind power due to their inadequate nonlinear fitting abilities. Deep learning (DL) excels at describing complex relationships. However, it is typically not applicable to WTPC modeling with a single wind speed input. This study proposes a novel data-driven DL approach mELM-CA-CNN to establish WTPCs based on multiple extreme learning machines (ELMs), channel attention (CA), convolutional neural network (CNN), and Huber loss (HL). First, multiple ELMs map a single wind speed to various high-dimensional feature spaces. Then, CA helps reduce redundant mappings of ELMs. Next, CNN extracts important features from all ELM mappings and models the complex relationship between wind speed and the corresponding power. Finally, the proposed model is trained with the differentiable and robust HL. To reduce the adverse impact of outliers on WTPC modeling, a segmented data cleaning approach based on 3σ criterion and quartile algorithm is proposed. Comparisons with some popular WTPC models demonstrate that mELM-CA-CNN obtains the most accurate WTPCs on four wind datasets, showing the superiority of the proposed DL approach. Moreover, the roles of the different modules of mELM-CA-CNN in improving model performance are verified.
Keywords: Wind turbine power curve modeling; Data cleaning; Extreme learning machine; Channel attention; Convolutional neural network; Huber loss (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422300302X
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:270:y:2023:i:c:s036054422300302x
DOI: 10.1016/j.energy.2023.126908
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).